by TortsDecember 11th, 2024

Too Long; Didn't Read

Results reveal the effectiveness of AI art protection tools against mimicry methods. Human evaluators show how artists’ styles can still be imitated despite safeguards.

Table of Links

Abstract and 1. Introduction

2. Background and Related Work

3. Threat Model

4. Robust Style Mimicry

5. Experimental Setup

6. Results

   6.1 Main Findings: All Protections are Easily Circumvented

   6.2 Analysis

7. Discussion and Broader Impact, Acknowledgements, and References

A. Detailed Art Examples

B. Robust Mimicry Generations

C. Detailed Results

D. Differences with Glaze Finetuning

E. Findings on Glaze 2.0

F. Findings on Mist v2

G. Methods for Style Mimicry

H. Existing Style Mimicry Protections

I. Robust Mimicry Methods

J. Experimental Setup

K. User Study

L. Compute Resources

6 Results

In Figure 4, we report the distribution of success rates per artist (N=10) for each scenario. We averaged the quality and stylistic transfer success rates to simplify the analysis (detailed results can be found in Appendix C). Since the forger can try multiple mimicry methods for each prompt, and then decide which one worked best, we also evaluate a “best-of-4” method that picks the most successful mimicry method for each generation (according to human evaluators).